i will introduce some recent progress towards understanding the scalability of markov chain monte carlo (mcmc) methods and their comparative advantage with respect to variational inference. i will fact-check the folklore that "variational inference is fast but biased, mcmc is unbiased but slow". i will then discuss a combination of the two via reverse diffusion, which holds promise of solving some of the multi-modal problems. this talk will be motivated by the need for bayesian computation in reinforcement learning problems as well as the differential privacy requirements that we face.